Organic Knowledge Categorization in Knowledge Management Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current customer relationship management systems face challenges in efficiently providing relevant information to users, particularly non-expert users, due to information overload and the high cost of implementing advanced AI mechanisms for expert modeling in knowledge management systems, leading to suboptimal inquiry resolution.
Innovation Solution
The implementation of a method and system for organically ranked knowledge categorization in a knowledge management system, which uses self-learning scores and user expertise weighting to suggest and adjust categories for answer content, allowing for proactive relevance ranking and categorization of answer content without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a search engine is deployed for information retrieval, then users can access information sources, but non-expert users cannot efficiently retrieve relevant information due to information overload
Solution Approach 1:
The patent replaces manual search engine operations with an automated expert modeling system that uses AI technologies and customized representation schemes to automatically rank and present relevant articles, substituting the mechanical search process with an intelligent automated system
Solution Approach 2:
The system enables self-service by automatically analyzing user questions, retrieving relevant articles from the knowledge base, and presenting ranked results without requiring user expertise in search techniques or information organization
2Measurement precision
If AI-based expert modeling is implemented in KM systems, then inquiry resolution accuracy improves, but implementation cost increases significantly
Solution Approach 1:
The patent uses copying by creating simplified representations of expert knowledge through customized representation schemes that capture essential problem-domain patterns without requiring full replication of complex AI mechanisms, enabling deployment in resource-constrained environments
Solution Approach 2:
The system changes parameters by adjusting the level of AI complexity and resource allocation based on specific implementation needs, allowing the expert modeling system to be configured for different organizational contexts and resource availability levels
3Reliability
If manual data collection processes are used for expert modeling, then domain expertise metrics can be obtained, but implementation cost and time increase
Solution Approach 1:
The patent applies preliminary action by pre-collecting and organizing domain expertise data during system setup, creating a foundation of ranked articles and metrics that can be automatically processed and applied to future inquiries without repeated manual data collection
Solution Approach 2:
The system implements feedback mechanisms where user interactions with ranked articles and system performance metrics are continuously collected and used to refine and update the expert modeling, automatically improving data quality over time without additional manual intervention
Data Source
AI summary
Embodiments of the present invention address deficiencies of the art in respect to expert modeling in a KM system and provide method, system and computer program product for organically ranked knowledge and categorization for a KM system. In one embodiment of the invention, a method for organically ranked knowledge and categorization in a KM system can be provided. The method can include bookmarking answer content for a first end user of the knowledge management system, suggesting a set of categories previously associated with the answer content by other end users of the knowledge management system, and categorizing the bookmarked answer content with a category selected from the set of categories.